Article(id=1309640043367588709, tenantId=1146029695717560320, journalId=1189621681917173762, issueId=1309640041656316677, articleNumber=null, orderNo=null, doi=10.19620/j.cnki.1000-3703.20250756, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=null, receivedDateStr=null, revisedDate=1767456000000, revisedDateStr=2026-01-04, acceptedDate=null, acceptedDateStr=null, onlineDate=1790172118274, onlineDateStr=2026-09-23, pubDate=1782230400000, pubDateStr=2026-06-24, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1790172118274, onlineIssueDateStr=2026-09-23, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1790172118274, creator=13701087609, updateTime=1790172118274, updator=13701087609, issue=Issue{id=1309640041656316677, tenantId=1146029695717560320, journalId=1189621681917173762, year='2026', volume='', issue='6', pageStart='1', pageEnd='62', issueExtLink='null', onlineDate='null', pubDate='1782230400000', pubDateStr='2026-06-24', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1790172117866, creator='13701087609', updateTime=1790232797347, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1309894549917495577, tenantId=1146029695717560320, journalId=1189621681917173762, issueId=1309640041656316677, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1309894549917495578, tenantId=1146029695717560320, journalId=1189621681917173762, issueId=1309640041656316677, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=21, endPage=29, ext={EN=ArticleExt(id=1309640044164506470, articleId=1309640043367588709, tenantId=1146029695717560320, journalId=1189621681917173762, language=EN, title=Research on Parallel Parking Path Planning in Complex Parking Scenarios, columnId=1309640042239324934, journalTitle=Automobile Technology, columnName=Special Topic on Intelligent Driving Path Planning Technology for Complex Scenarios, runingTitle=null, highlight=null, articleAbstract=

To address issues such as long planning time and low success rate in automated parking under complex scenarios, this paper proposes a parallel parking path planning algorithm based on a Smart Probability Map-enhanced Bidirectional Rapidly-exploring Random Tree* (SPM-Bi-RRT*). 3 types of complex parking scenarios are defined: narrow parking spaces, constrained passageways, and obstacle-present environments. A simplified vehicle collision detection model is established using the vector cross product method. The sampling strategy is innovatively improved by hierarchically partitioning the sampling space to reduce computational complexity. A probability map is constructed based on repulsive bias and prior paths to guide algorithmic sampling. Path extension is performed using improved Reeds-Shepp curves with adaptive dynamic step sizes. Finally, path smoothing and optimization are achieved through pruning and cubic B-spline curves. Simulation results show that the proposed SPM-Bi-RRT* algorithm can generate smooth, collision-free paths in all three complex parking scenarios. Compared with the original RRT* algorithm, the search time is reduced by 56.67%, 49.63%, and 58.42%, respectively, and the path length is shortened by 7.74%, 3.84%, and 3.65%.

, authors=Yunqing Xie, Yanping Zheng, Fei Ju, authorsList=Yunqing Xie, Yanping Zheng, Fei Ju, authorCompany=null, correspAuthors=Yanping Zheng, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1309640044625879911, articleId=1309640043367588709, tenantId=1146029695717560320, journalId=1189621681917173762, language=CN, title=复杂泊车场景下平行泊车路径规划研究, columnId=1309640042377736968, journalTitle=汽车技术, columnName=复杂场景中智能驾驶路径规划技术专题, runingTitle=null, highlight=null, articleAbstract=

针对复杂泊车场景中自动泊车规划时间长、路径质量欠佳等问题，提出了一种基于智能概率地图的双向快速随机搜索树（SPM-Bi-RRT*）平行泊车路径规划算法。定义了3种复杂泊车场景，即狭窄车位、通道受限及存在障碍物场景，并基于向量叉乘法建立了简化车辆的碰撞检测模型。在采样策略方面，通过逐级限制采样区域，引入斥力偏置和先验路径，构建了基于概率地图的智能采样机制，从而提升了算法在搜索空间中的采样效率。同时，改进RS曲线的扩展代价并采用自适应动态步长策略，进一步结合剪枝操作与B样条曲线对冗余路径进行优化处理，有效提高了路径规划的成功率和搜索效率。仿真结果表明，提出的SPM-Bi-RRT*算法在3种复杂泊车场景中均能规划出平滑且无碰撞的路径，相较于原始RRT*算法，搜索时间分别减少56.67%、49.63%、58.42%，路径长度分别缩短7.74%、3.84%、3.65%。

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